title: Long-range correlations in stride intervals may emerge from non-chaotic walking dynamics.
abstract: Stride intervals of normal human walking exhibit long-range temporal correlations. Similar to the fractal-like behaviors observed in brain and heart activity, long-range correlations in walking have commonly been interpreted to result from chaotic dynamics and be a signature of health. Several mathematical models have reproduced this behavior by assuming a dominant role of neural central pattern generators (CPGs) and/or nonlinear biomechanics to evoke chaos. In this study, we show that a simple walking model without a CPG or biomechanics capable of chaos can reproduce long-range correlations. Stride intervals of the model revealed long-range correlations observed in human walking when the model had moderate orbital stability, which enabled the current stride to affect a future stride even after many steps. This provides a clear counterexample to the common hypothesis that a CPG and/or chaotic dynamics is required to explain the long-range correlations in healthy human walking. Instead, our results suggest that the long-range correlation may result from a combination of noise that is ubiquitous in biological systems and orbital stability that is essential in general rhythmic movements.
authors.affiliation: Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
authors.forename: Jooeun
authors.initials: J
authors.lastname: Ahn
authors.forename: Neville
authors.initials: N
authors.lastname: Hogan
mesh_terms: D006801:Humans; D017711:Nonlinear Dynamics; D016138:Walking